ABSTRACT: Quantifying cardiovascular calcification at very old age for personalized risk classification Predicted risk is central to decision-making in primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, classifying risk in older adults ?75 years remains extremely challenging. By placing heavy weight on age, existing risk prediction models universally assign high risk status and therefore recommend treatment for nearly all older adults, raising concerns about overmedication, drug-drug interactions, and lack of personalization. Competing risk of mortality and morbidity due to other conditions (e.g., cancer, lung disease, and dementia) is another issue in this population, requiring a fine balance between prevention of CVD based on accurate risk prediction vs. management of other comorbidities. Unfortunately, such accurate risk prediction is not achievable with one-time assessment of traditional cardiovascular risk factors at older ages because this approach does not capture cumulative lifetime exposures and individuals' susceptibility. In this context, coronary artery calcium (CAC) is promising, as it directly quantifies a composite of cumulative exposure and an individual's susceptibility to risk factors. Indeed, a high CAC score is one of the most potent predictors of ASCVD. Recently, zero and low CAC has been shown to be useful for ?de-risking?, identifying individuals who are at low absolute risk in whom preventive therapy may not result in net benefit. Recent studies also demonstrate that extra-coronary calcium (ECC) (i.e., aortic root/valve, mitral annulus, and thoracic aorta) detected on a routine CAC scan provides risk information beyond CAC. However, prevalence and prognostic data for CAC and ECC among very old adults are surprisingly sparse. Thus, we propose to perform non-contrasted cardiac-gated computed tomography among ~3,100 participants in the Atherosclerosis Risk in Communities (ARIC) Study during forthcoming visits between 2017-19 and to develop a dedicated CVD risk classification tool incorporating CAC and ECC for older adults ?75 years. To maximize the usefulness of our CVD risk classification tool, we will develop benefit-harm charts incorporating our prediction models and patient preferences for two representative scenarios, preventive statin and aspirin pharmacotherapy. Aim 1: To develop risk classification models for CVD risk in the 75-and-older population. Aim 2: To evaluate the interplay between 30-year cumulative exposures and CAC/ECC for assessment of healthy vascular aging (e.g., low or zero CAC) and estimation of CVD prognosis. Aim 3: To evaluate the effect of preventive treatment preferences and CAC/ECC-based risk information on the balance of benefits and harms of preventive pharmacotherapy in adults ?75 years of age. This project will deliver accurate risk classification tools for CVD based on established risk factors and CAC/ECC for the 75-and-older population. The comparison of 30-year cumulative risk factor exposures vs. CAC/ECC will be of value clinically and biologically. Our benefit-harm charts will directly inform evidence-based shared decision-making in the context of primary prevention of CVD among older adults aged 75 and older.